Crash Reproduction in Android Apps with Stack Trace Only

PhD Thesis Proposal Defence


Title: "Crash Reproduction in Android Apps with Stack Trace Only"

by

Miss Maryam Alsadat MASOUDIAN TARGHI


Abstract:

As Android applications continue to proliferate, the rising number of 
reported crashes highlights the critical need for safe and reliable 
development practices. However, issue tracking systems like GitHub often 
lack detailed reproduction steps for about 80% of reported cases, with stack 
traces alone insufficient for effective debugging and crash reproduction. 
Developers face significant challenges in verifying whether a crash has been 
resolved due to the vast input space of Android apps, primarily accessed via 
Graphical User Interfaces (GUIs), and varying environmental settings.

This thesis proposes two solutions aimed at narrowing the input space to 
those that directly contribute to crashes, facilitating effective 
reproduction of the crashes. The first solution applies a directed fuzzing 
strategy to concentrate testing on necessary GUI inputs directly tied to 
crashes. It integrates Attribute-Sensitive Reachability analysis, which 
simulates the app’s visual state to statically track widget attributes and 
predict the relevant events from the irrelevant ones leading to crashes 
before execution. Evaluation on the Themis benchmark shows our directed 
fuzzing solution reduces crash reproduction time significantly—from six 
hours down to two hours. The second solution introduces a Neuro-Symbolic 
approach that combines static program analysis with Large Language Models 
(LLMs) to infer environmental settings in an Android smartphone that impact 
the crash occurrences. It relies on the relevancy of API methods’ 
functionality to environment settings in an Android device to predict 
possible environment settings affecting a crash occurrence. Using program 
slicing allows our solution to identify data and control dependencies around 
crash points, then correlates API functionalities with environment 
configurations by leveraging LLM-derived specifications. This approach 
achieves more than 80% recall and precision in detecting relevant settings 
for 50 crashes collected from highly starred open-sourced Android app 
repositories in GitHub. Together, our solutions empower developers with 
effective tools to reproduce crashes more efficiently, ultimately improving 
the reliability and user experience of Android applications.


Date:                   Tuesday, 3 June 2025

Time:                   10:00am - 12:00noon

Venue:                  Room 3494
                        Lifts 25/26

Committee Members:      Prof. Charles Zhang (Supervisor)
                        Dr. Dimitris Papadopoulos (Chairperson)
                        Dr. Shuai Wang